vignettes/pkgdown/simulater.Rmd
simulater.Rmd
Simulation for decision analysis
Start by selecting the types of variables to use in the simulation from the Select types
dropdown in the Simulate tab. Available types include Binomial, Constant, Discrete, Log normal, Normal, Uniform, Data, Grid search, and Sequence.
Add random variables with a binomial distribution using the Binomial variables
inputs. Start by specifying a Name
(crash
), the number of trials (n) (e.g., 20) and the probability (p) of a success
(.01). Then press the icon. Alternatively, enter (or remove) input directly in the text input area (e.g., crash 20 .01).
List the constants to include in the analysis in the Constant variables
input. You can either enter names and values directly into the text area (e.g., cost 3
) or enter a name (cost
) and a value (5) in the Name
and Value
input respectively and then press the icon. Press the icon to remove an entry. Note that only variables listed in the (larger) text-input boxes will be included in the simulation.
Define random variables with a discrete distribution using the Discrete variables
inputs. Start by specifying a Name
(price
), the values (6 8), and their associated probabilities (.3 .7). Then press the icon. Alternatively, enter (or remove) input directly in the text input area (e.g., price 6 8 .3 .7). Note that the probabilities must sum to 1. If not, a message will be displayed and the simulation cannot be run.
To include log normally distributed random variables in the analysis select Log Normal
from the Select types
dropdown and use Log-normal variables
inputs. See the section Normal
below for additional information.
To include normally distributed random variables in the analysis select Normal
from the Select types
dropdown and use Normal variables
inputs. For example, enter a Name
(demand
), the Mean
(1000) and the standard deviation (St.dev.
, 100). Then press the icon. Alternatively, enter (or remove) input directly in the text input area (e.g., demand 1000 100
).
The Poisson distribution is useful to simulate the number of times and event occurs in a particular time span, such as the number of patients arriving in an emergency room between 10 and 11pm. To include Poisson distributed random variables in the analysis select Poisson
from the Select types
dropdown and use Poisson variables
inputs. For example, enter a Name
(patients
) and a value for the number of occurrences Lambda
the event of interest (20). Then press the icon. Alternatively, enter (or remove) input directly in the text input area (e.g., patients 20
).
To include uniformly distributed random variables in the analysis select Uniform
from the Select types
dropdown. Provide parameters in the Uniform variables
inputs. For example, enter a Name
(cost
), the Min
(10) and the Max
(15) value. Then press the icon. Alternatively, enter (or remove) input directly in the text input area (e.g., cost 10 15
).
To include variables from a separate data-set in the calculations specified in the Simulation formulas
input box, choose a data-set from the Input data for calculations
dropdown. This can be very useful in combination with the Grid search
feature for portfolio optimization. However, when used in conjunction with other inputs care must be taken to ensure the number of values returned by different calculations is the same. Otherwise you will see an error like:
Error: arguments imply differing number of rows: 999, 3000
To include a sequence of values select Grid search
from the Select types
dropdown. Provide the minimum and maximum values as well as the step-size in the Grid search
inputs. For example, enter a Name
(price
), the Min
(4), Max
(10), and Step
(0.01) value. If multiple variables are specified in Grid search
all possible value combinations will be created and evaluated in the simulation. For example, suppose a first variable is defined as x 1 3 1
and a second as y 4 5 1
in the Grid search
text input then the following data is generated:
x | y |
---|---|
1 | 4 |
2 | 4 |
3 | 4 |
1 | 5 |
2 | 5 |
3 | 5 |
Note that if Grid search
has been selected the number of values generated will override the number of simulations or repetitions specified in # sims
or # reps
. If this is not what you want use Sequence
. Then press the icon. Alternatively, enter (or remove) input directly in the text input area (e.g., price 4 10 0.01
).
To include a sequence of values select Sequence
from the Select types
dropdown. Provide the minimum and maximum values in the Sequence variables
inputs. For example, enter a Name
(trend
), the Min
(1) and the Max
(1000) value. Note that the number of ‘steps’ is determined by the number of simulations. Then press the icon. Alternatively, enter (or remove) input directly in the text input area (e.g., trend 1 1000
).
To perform a calculation using the generated variables, create a formula in the Simulation formulas
input box in the main panel (e.g., profit = demand * (price - cost)
). Formulas are used to add (calculated) variables to the simulation or to update existing variables. You must specify the name of the new variable to the left of a =
sign. Variable names can contain letters, numbers, and _
but no other characters or spaces. You can enter multiple formulas. If, for example, you would also like to calculate the margin in each simulation press return
after the first formula and type margin = price - cost
.
Many of the same functions used with Create
in the Data > Transform tab and in Filter data
in Data > View can also be included in formulas. You can use >
and <
signs and combine them. For example x > 3 & y == 2
would evaluate to TRUE
when the variable x
has values larger than 3 AND y
has values equal to 2. Recall that in R, and most other programming languages, =
is used to assign a value and ==
to evaluate if the value of a variable is exactly equal to some other value. In contrast !=
is used to determine if a variable is unequal to some value. You can also use expressions that have an OR condition. For example, to determine when Salary
is smaller than $100,000 OR larger than $20,000 use Salary > 20000 | Salary < 100000
. |
is the symbol for OR and &
is the symbol for AND (see also the help file for Data > View).
A few additional examples of formulas are shown below:
z = x - y
logical
variable z that takes on the value TRUE when x > y and FALSE otherwisez = x > y
logical
z that takes on the value TRUE when x is equal to y and FALSE otherwisez = x == y
ifelse
. Note the similarity to if
statements in Excelz = ifelse(x < y, TRUE, FALSE)
ifelse
statements can be used to create more complex (numeric) variables as well. In the example below, z will take on the value 0 if x is smaller than 60. If x is larger than 100, z is set equal to 1. Finally, when x is 60, 100, or between 60 and 100, z is set to 2. Note: make sure to include the appropriate number of opening (
and closing )
brackets!z = x - mean(x)
z = abs(x)
price
that maximizes profit
use the find_max
command. In this example price
could be a random or Sequence variable
. There is also a find_min
command.optimal_price = find_max(profit, price)
pmin
and pmax
. In the example below, z will take on the value of x when x is larger than y and take on the value of y otherwise.z = pmax(x, y)
See the table below for an example:
x | y | pmax(x,y) |
---|---|---|
1 | 0 | 1 |
2 | 3 | 3 |
3 | 8 | 8 |
4 | 2 | 4 |
5 | 10 | 10 |
pmin
and pmax
a number of functions are available to calculate summary statics across multiple variables. For example, psum
calculates the sum of elements across different vectors. See https://radiant-rstats.github.io/radiant.data/reference/pfun.html for more information.z = psum(x, y)
See the table below for an example:
x | y | psum(x,y) |
---|---|---|
1 | 0 | 1 |
2 | 3 | 5 |
3 | 8 | 11 |
4 | 2 | 6 |
5 | 10 | 15 |
Other commonly used functions are ln
for the natural logarithm (e.g., ln(x)
), sqrt
for the square-root of x (e.g., sqrt(x)
) and square
to calculate square of a variable (e.g., square(x)
).
To return a single value from a calculation use functions such as min
, max
, mean
, sd
, etc.
sdw
. It takes weights and variables as inputs and returns the standard deviation of the weighted sum of the variables. For example, to calculated the standard deviation for a portfolio of three stocks (e.g., Boeing, GM, and Exxon) you could use the equation below in the Simulation formulas
input. f
and g
could be values (e.g., 0.2 and 0.8) or vectors of different weights specified in a Grid search
input (see above). Boeing
, GM
, and Exxon
are names of variables in a data-set included in the simulation using a Data
input (see above).Pstdev = sdw(f, g, 1-f-g, Boeing, GM, Exxon)
For an example of how the simulate tool could be used for portfolio optimization see the state-file available for download here
It is possible that the standard functions available in R are not sufficiently flexible to conduct the simulation you have in mind. If this is the case, click on the Add functions
check box on the bottom left of your screen and can create your own custom function in the Simulation functions
input box in the main panel. To learn about writing R-functions see https://www.statmethods.net/management/userfunctions.html for a good place to start.
For an example of how to use custom R-functions in a gambling simulation, see the state-file available for download here. The report generated through Report > Rmd provides additional information about the simulation setup and the use of functions.
The value shown in the # sims
input determines the number of simulation draws. To redo a simulation with the same randomly generated values, specify a number in the Set random seed
input (e.g., 1234).
To save the simulated data for further analysis, specify a name in the Simulated data
input box. You can then investigate the simulated data by choosing the data with the specified name from the Datasets
dropdown in any of the Data tabs (e.g., Data > View, Data > Visualize, or Data > Explore).
When all required inputs have been specified press the Simulate
button to run the simulation.
In the screen shot below var_cost
and fixed_cost
are specified as constants. E
is normally distributed with a mean of 0 and a standard deviation of 100. price
is a discrete random variable that is set to $6 (30% probability) or $8 (70% probability). There are three formulas in the Simulation formulas
text-input. The first establishes the dependence of demand
on the simulated variable price
. The second formula specifies the profit function. The final formula is used to determine the number (and proportion) of cases where profit is below 100. The result is assigned to a new variable profit_small
.
In the output under Simulation summary
we first see details on the specification of the simulation (e.g., the number of simulations). The section Constants
lists the value of variables that do not vary across simulations. The sections Random variables
and Logicals
list the outcomes of the simulation. We see that average demand
in the simulation is 627.94 with a standard deviation of 109.32. Other characteristics of the simulated data are also provided (e.g., the maximum profit is 1758.77). Finally, we see that the probability of profits
below 100 is equal 0.32 (i.e., profits were below $100 in 315 out of the 1,000 simulations).
To view histograms of the random variables as well as the variables created using Simulation formulas
ensure Show plots
is checked.
Because we specified a name in the Simulated data
box the data are available as simdat
within Radiant (see screen shots below). To use the data in Excel click the download icon on the top-right of the screen in the Data > View tab or go to the Data > Manage tab and save the data to a csv file (or use the clipboard feature). For more information see the help file for the Data > Manage tab.
Suppose the simulation discussed above was used to get a better understanding of daily profits. To develop insights into annual profits we could re-run the simulation 365 times. However, this can be done more conveniently by using the functionality available in the Repeat tab. First, select the Variables to re-simulate
, here E
and price
. Then select the variable(s) of interest in the Output variables
box (e.g., profit
). Set # reps
to 365.
Next, we need to determine how to summarize the data. If we select Simulation
in Group by
the data will be summarized for each draw in the simulation across 365 repeated simulations resulting in 1,000 values. If we select Repeat
in Group by
the data will be summarized for each repetition across 1,000 simulations resulting in 365 values. If you imagine the full set of repeated simulated data as a table with 1,000 rows and 365 columns, grouping by Simulation
will create a summary statistic for each row and grouping by Repeat
will create a summary statistic for each column. In this example we want to determine the sum
of simulated daily profits across 365 repetitions so we select Simulation
in the Group by
box and sum
in the Apply function
box.
To determine, the probability that annual profits are below $36,500 we enter the formula below into the Repeated simulation formula
text input.
profit_365 = profit_sum < 36500
Note that profit_sum
is the sum
of repeated simulations of the profit
variable defined in the Simulate tab. When you are done with the input values click the Repeat
button. Because we specified a name for Repeat data
a new data set will be created. repdat
will contain the summarized data grouped per simulation (i.e., 1,000 rows). To store all 365 x 1,000 simulations/repetitions select none
from the Apply function
dropdown.
Descriptive statistics for the repeated simulation are shown in the main panel under Repeated simulation summary
. We see that the annual expected profit (i.e., the mean of profit_sum
) for the company is 172,311.84 with a standard deviation of 10,772.29. Although we found above that daily profits can be below $100, the chance that profits are below \(365 \times 100\) for the entire year are slim to none (i.e., the proportion of repeated simulations with annual profits below $36,500 is equal to 0).
If Show plots
is checked a histogram of annual profits (profit_sum
) is shown under Repeated simulation plots
. There is no plot for profit_365
because it has only one value (i.e., FALSE).
The state-file for the example in the screenshots above is available for download here
For a simple example of how the simulate tool could be used to find the price that maximizes profits see the state-file available for download here
Note that the Repeat tab also has the option to use a Grid search
input to repeat a simulation by replacing one or more Constants
specified in the Simulation
tab in an iterative fashion. This input option is shown only when Group by
is set to Repeat
. Provide the minimum and maximum values as well as the step-size in the Grid search
inputs. For example, enter a Name
(price
), the Min
(4), Max
(10), and Step
(0.01) value. If multiple variables are specified in Grid search
all possible value combinations will be created and evaluated in the simulation. Note that if Grid search
has been selected the number of values generated will override the number of repetitions specified in # reps
. Then press the icon. Alternatively, enter (or remove) input directly in the text area (e.g., price 4 10 0.01
).
Add code to Report > Rmd to (re)create the analysis by clicking the icon on the bottom left of your screen or by pressing ALT-enter
on your keyboard.
If a plot was created it can be customized using ggplot2
commands or with patchwork
. See example below and Data > Visualize for details.
plot(result, custom = TRUE) %>% wrap_plots(plot_list, ncol = 2) + plot_annotation(title = "Simulation plots")
For an overview of related R-functions used by Radiant to construct and evaluate (repeated) simulation models see Model > Simulate.
Key functions from the stats
package used in the simulater
tool are rbinom
, rlnorm
, rnorm
, rpios
, and runif
Copy-and-paste the full command below into the RStudio console (i.e., the bottom-left window) and press return to gain access to all materials used in the simulation module of the Radiant Tutorial Series:
usethis::use_course("https://www.dropbox.com/sh/72kpk88ty4p1uh5/AABWcfhrycLzCuCvI6FRu0zia?dl=1")
Setting Up a Simulation in Radiant (#1)
Setting Up a Repeated Simulation in Radiant (#2)
Using simulation to solve probability questions (#3)
ifelse
to specify a simulation formulapmax
to specify a simulation formulaUsing Grid Search in Simulation (#5)
find_max
function